Question type-sensitive answer summarization

ABSTRACT

In a question answering system, the system identifies a type of question input by a user. The system then generates answer summaries that summarize answers to the input question in a format that is determined based on the type of question asked by the user. The answer summaries are output, in the corresponding format, in answer to the input question.

BACKGROUND

There are many different types of techniques for discovering information, using a computer network. One specific technique is referred to as a community-based question and answering service (referred to as cQA). The cQA service is a kind of web service through which people can post questions and also answer other people's questions on a web site. The growth of cQA has been relatively significant, and it has recently been offered by commercially available web search engines.

In current cQA services, a community of users either subscribes to the service, or simply accesses the service through a network. The users in the community can then post questions that are viewable by other users in the community. The community users can also post answers to questions that were previously submitted by other users. Therefore, over time, cQA services build up very large archives of previous questions and answers posted for those previous questions.

In some current cQA systems, once the user who has asked a question and has received a sufficient number of answers, the user can then choose the best answer from those answers that have been received. The user may also simply leave it to the community to vote for the best answer. This results in large question and answer archives, representing knowledge repositories, that can be used later.

For instance, these repositories can be used to expedite answers to the user. In other words, there is typically a lag time between the time when a user in the community posts a question, and the time when other users of the community post answers to that question. In order to avoid this lag time, some cQA services automatically search the archive of questions and answers to see if the same question has previously been asked. If the question is found in the archives, then one or more previous answers can be provided, in answer to the current question, with very little delay. This type of searching for previous answers is sometimes referred to as “question search” or “on-line search”.

The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.

SUMMARY

In a question answering system, the system identifies a type of question input by a user. The system then generates answer summaries that summarize answers to the input question in a format that is determined based on the type of question asked by the user. The answer summaries are output, in the corresponding format, in answer to the input question.

This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating one embodiment of an answer summarization system.

FIG. 2 is a flow diagram illustrating the overall operation of the system shown in FIG. 1.

FIGS. 2A and 2B illustrate question and answer taxonomies, respectively, that are used, in one embodiment, in order to identify question type.

FIG. 3 is a block diagram of one illustrative embodiment of a question answering system.

FIG. 4 is a block diagram illustrating one embodiment of the overall operation of the system shown in FIG. 3.

FIG. 5 is a block diagram of one embodiment of a computing environment.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of one embodiment of a question summarization system 100. System 100 includes question-focused summarization component 102 that, itself, includes question type identifier 104 and answer summarization component 106. Component 102 is shown having access to question/answer data store 108. Question/answer data store 108 is illustratively an archive of questions input in a community question answering system, along with the answers posted for those questions. Component 102 accesses the questions and answers in data store 108 and summarizes the answers based on the question type, and outputs the questions with summarized answers to data store 110.

FIG. 2 is a flow diagram illustrating one embodiment of the overall operation of the system shown in FIG. 1. Component 102 first receives, or accesses, the questions and answers in data store 108. This is indicated by block 120 in FIG. 2. Next, question type identifier 104 in component 102 identifies the type of a selected question. This is indicated by block 122 in FIG. 2.

Answer summarization component 106 then generates answer summaries, in a form corresponding to the type of the question identified. This is indicated by block 124 in FIG. 2. In doing so, component 106 attempts to summarize the answers that were identified by users as the best answers to the question being processed.

In analyzing the types of questions asked in cQA services, it has been found that some types of questions have best answers that are reusable in later searching, while others do not. For instance, questions that ask for answers that are facts may likely have a best answer that is extremely useful in later searching. However, cQA questions that ask for an opinion may not be helpful in later information searching.

For example, a fact-based, closed ended question, such as “What was the birth date of Abraham Lincoln?” will have one unique answer. Therefore, an answer that is labeled in a cQA system as the best answer to that question will likely be reusable and very relevant in later information searching. However, a question such as “What actress has the most seductive voice?” asks for an opinion. Therefore, even the answer labeled by the community as the best answer is still only an opinion. Thus, those answers may not be as reusable in later information searching processes.

Similarly, even fact-based open-ended questions may not be as reusable as a closed-ended question. For instance, a question such as “Does anyone know of any movie stars having birthdays coming up soon?” is open ended. First of all, the time period of the question is relevant because it asks for birthdays that are “coming up soon”. Similarly, there may be a wide variety of different people answering the question, and so they may provided a wide variety of different birthdays for different movie stars, depending on the type of movie that they enjoy. Thus, answers to this type of fact-based open-ended question, even if they are labeled as “best answers” may be less reusable or less relevant to later information searching processes.

Therefore, in order to generate summaries of answers to cQA questions, question type identifier 104 identifies the question type, and answer summarization component 106 identifies the types of answers to the identified question, and generates a summary of the best answers (and optionally other answers as well) in a format that is determined based on the type of the question identified.

FIG. 2A shows a taxonomy 200 that can be used in identifying a type of answer to a question. Taxonomy 200 is based on whether the best answer can be reused when a question that is similar to the best answer's question is asked again. Answer type taxonomy 200 first divides best answers 202 to cQA questions into two categories. Those categories are reusable 204 and not reusable 206. A reusable best answer means that it can be reused as the answer if a similar question, to the question that it answered, is asked again. A not reusable best answer means that it cannot be reused.

The reusable best answers are further divided into factual best answers 208 and subjective best answers 210. A factual best answer is a fact that can be used as the best answer, while a subjective best answer is an opinion type of answer that can be used as the best answer.

The factual best answers 208 can be divided into two further types, those being unique best answers 212 and non-unique best answers 214. A unique best answer has only a single best answer to its question. A non-unique best answer has alternative best answers.

These alternative (non-unique) best answers 214 can be divided into direct answers 216 and indirect best answers 218. A direct answer is just that, an answer that answers its question directly. An indirect answer is an answer which answers the question through inference.

The non-reusable best answers 206 are divided into two types: relevant best answers 220 and irrelevant best answers 222. Relevant best answers 220 are not reusable as a best answer to its question, if that question is asked at a later time, but they are relevant to the question. An irrelevant best answer is not reusable as a best answer to its question and it is irrelevant to its question.

Table 1 summarizes answer type taxonomy 200 and gives examples for each answer type.

TABLE 1 Summary of cQA Answer Taxonomy with Examples Type Description Example Reusab Factua Unique BA is an accurate and complete Question: Which country has the largest population? answer for its question. BA: China Not Unique Direct BA is one of the best answers to Question: How can I recover data erased on my computer? its question. BA: There's a program I used to have called ‘ABC Utilitities 2006’, I think you can recover lost files with it. It's not free but you can download a free 30 day trial http://www.ABC.com/ Hope this works for you A: ABC's GetDataBack . . . A: http://www.undelete.com/file-recovery.as . . . God Bless A: . . . use SYSTEM RESTORE . . . Indirect BA is the best answer to the Question: Where can i download duckwire from because inferred goal of its question. every website i go on wants to charge me? There are also user responses A: Go to duckwire.com and download the basic version, answering its question directly. which is free. BA: you may as well just avoid all the confusion and get chickwire then: http://www.duckwire.com/ it's identical to duckwire, but open source so it'll always be free Subjective BA is one of the best subjective Question: What is the best sci-fi movie u ever saw? answers for its question. Other A: XYZ Movie subjective answers might be A: DEF Movie chosen as the best answer if BA: GHI Movie askers are different. Not Reusable Relevant BA is relevant to its question Question: Why was “The ABC Song” by John Doe so but it cannot be reused. shortlived? BA: I'm not sure where you live, but in NJ, especially South Jersey, that song was played out. It may have made the New Year's “We're never playing this song again” list. Irrelevant BA is unrelated to the question Question: How to forward e-mail without the email itself. addresses in the To Box? BA: It appears that the question period has expire. If an answer has been given that meets your needs, please pick a ‘best answer’. Regards. A: put those e-mails that you don't want no one to see in the bcc box and then send BA's question is nonsense so is Question: *oink* Whose gunna partay with me? its anwers. BA: I'll party w/you I'm old enough to drink but it's over rated. I'll bring the cheese in the can HAPPY NEW YEAR

Table 1 first shows a reusable, factual, unique best answer. This best answer type provides enough useful information in the answer and no other answers add more information. For instance, if a question asks “Which country has the largest population?” and the best answer is “China”. This answer is accurate and complete, so it can be categorized as the unique type.

The difference between direct and indirect answer types is a bit subtle. A direct best answer answers its question in a straightforward manner; while an Indirect best answer answers its question in an inferred manner. Answerers of Indirect best answers usually look beyond the surface level of the question and try to give answers that satisfy the inferred needs behind question. For example, a question asked “Where can i download fatwire from because every website i go on wants to charge me?” and the best answer was “http://www.duckwire.com/”. This answer addresses the ‘free’ and ‘download’ parts of the question. However, instead of giving a FatWire download site, the BA suggested an alternative, i.e. DuckWire, that presumably would fulfill what the asker really wanted. Notice that a possible Direct BA suggested that download the basic version of FatWire from Fatwire.com that is free. Assume FatWire is a popular peer to peer (P2P) file sharing tool, and that it has two versions. The Basic version is free but the Pro version is not free. It can be inferred from the question that the asker wanted the Pro version and not the Basic. A true Direct BA is a site where the asker could download the Pro version without paying but it may very likely be an illegal site even if it does exist. Therefore the DuckWire suggestion is likely a very good alternative. Another example of Direct vs. Indirect BA is shown earlier in Table 1. The question asked celebrity birthdays in the coming month (November). A Direct BA contains a list of celebrity birthdays in November as shown in one of the answers in Table 1. A better alternative, i.e. an Indirect one, showed the asker where she can find this information herself not only for November but for all other months.

A Subjective BA answers questions that look for personal opinions or recommendations. For example, a question asked “What is the best sci-fi movie u ever saw?” Each answerer would have his or her own opinion about what the best sci-fi movie is. There might be consensus but it will likely not be unique. Many answers in the Entertainment & Music category are this type.

Answers that fall into the Not Reusable category could be relevant or irrelevant to their questions. Despite their relevance, they could not be used as the BAs. For example, a question asked “Why was “The ABC Song” by John Doe so shortlived?” A Relevant BA said “I'm not sure where you live, but in NJ, especially South Jersey, that song was played out. It may have made the New Year's “We're never playing this song again” list.” This BA seems relevant to the question but we could not be sure it is the BA without knowing where the asker resided.

There are many possible reasons that make a BA irrelevant. Two exemplary possible explanations are listed in Table 1. One is that the BA does not answer its question. For example, a question asked for how to forward an email without showing the email addresses in the To box. There is an answer that contains the correct method “put the email address into the bcc box”, but the best answer voted by one community is: “It appears that the question period has expired. If an answer has been given that meets your needs, please pick a ‘best answer’.” Another one is that the BA's question is nonsense and therefore its answers are as well. For example, someone asked “*oink* Whose gunna partay with me?” Though a BA was chosen, it does not make sense to reuse the BA.

FIG. 2B shows a question taxonomy 300 that can be used to categorize cQA questions and guide answer summarization. In taxonomy 300, a cQA question 302 can be divided into four top-level categories: navigational 304, informational 306, transactional 308, and social 310. A navigational question is one which intends to find the uniform resource locator (URL) of a web site that the questioner has in mind. An informational question intends to acquire information about a question topic. A transactional question intends to obtain or enlist interaction with people in the cQA communities on which the question is posted.

The informational question 306 includes questions that request information about topics, and can be divided into constant questions 312 and dynamic questions 314. Constant questions have a fixed or stable set of answers, while dynamic questions do not.

The dynamic questions 314 can be further divided into three categories, those including opinion 316, context dependent questions 318 and open questions 320. Opinion questions are those asking for opinions. Questions in this category seek opinions from people in the cQA community about what they think of someone, some event, some object, etc. Context-dependent questions 318 are those questions which have different answers, depending on the different context in which they are asked. For example, the question “What is the population of China?” will have different answers according to the date on which it is asked. Open questions 320 are those questions that ask for some facts or methods but have a variety of answers, or the answer itself may have unconstrained depth.

The transactional questions 308 can further be divided into possessive questions 322 and interactive questions 324. Possessive questions 322 indicate the askers intention to own the resource requests, regardless of whether a computer is required to make use of the required resource. The interactive questions 324 are questions looking for entertainment or utility resources.

Social questions 310 do not intend to obtain any given answer. These questions include telling jokes, and expressing the askers own ideas. For instance, one social question may be “Why do so many lazy people come on here simply just to ask . . . ?” The question is actually expressing a negative sentiment toward the large number of people who ask how to accomplish a certain task in the cQA community.

Table 2 illustrates question taxonomy 300, and gives examples of each type of question.

TABLE 2 Community Question Answering Service (cQA) Question Taxonomy with Examples Question Type Description Example Navigational The question asks about URL information of The official website of the us lottery specific websites. program for immigration? Informational The question asks for information about In a word ‘tetra-hadron’ what exactly something. ‘a-tetra’ denotes? Constant The question requires no additional What is the best sci-fi movie u ever information and has fixed answers. saw? Dynamic The question has wide ranges of answers. Who won XYZ Gameshow? Opinion The question seeks people's opinions of Can you list some birthdays of stars something. in the coming week? Context- The question needs additional context dependent information to limit the range of answers. Open The question has a variety of answers or the answer can have unconstrained depth. Transactional The question intends to get some Is there a computer program that lets resources. you create a planet? Possessive The question intends to find some files Installing ABC Software - serial that are downloadable or something that number? can be read off the screen or printed. A good weather website? Interactive The question intends to find some websites that one can log on it and do something. Social This question is just chatting, joking, or Ever been on a boat ride? expressing one's own idea and doesn't mind Two men who are sitting in a pub? to get an answer.

Identifying the question type and answer type as set out in FIG. 2 can be done automatically, or simply by asking the user to select (such as from a drop down box) the type of question or answer that is being provided.

In using these taxonomies and analyzing large archives of cQA responses, it has been found that a number of the different question types have answers, less than 50 percent of which can be reused in later searching given a similar type of question. Therefore, in one embodiment, answer summarization component 106 uses question-focused multi-document summarization techniques to generate the best answers for reuse in answering questions that are submitted again, later, in the cQA service.

Generating summaries is discussed in greater detail below with respect to Tables 3-8. In any case, however, once the summaries are generated, based on the type of question that the answers are provided for, the questions and answer summaries (which may be best answer summaries) are output for use in other information searching systems (such as in question answer systems). In that instance, the questions with summarized answers can be stored in data store 110 shown in FIG. 1. This is indicated by block 126 in FIG. 2.

Some illustrative embodiments of summarizing answers, based on question type, will now be described. In doing this, answer summarizer component 106 illustratively invokes one of a plurality of different summarization modules 1-N, one of which exists for each different question type. Examines of some illustrative summarization modules are shown in tables 3, 5, and 6. The present discussion proceeds with respect to applying question-focused multi-document summarization techniques to create answer summaries for open and opinion questions (questions 316 and 320) as shown in FIG. 2B. Of course, these techniques can be used with other question types as well, and the open and opinion questions are discussed herein for the sake of example only.

In one embodiment, answer summarization component 106 employs the summarization steps set out in Table 3 in order to summarize open-type questions 320. The steps in Table 3 follow multi-document summarization procedures which include topic identification, interpretation and fusion, and then summary generation. In the topic identification phase, answers are clustered using any of a wide variety of relatively simple clustering techniques. For instance, cosine measure, which is a known similarity measure, can be used to estimate answer similarity and an empirically determined threshold can be used to merge answers into clusters. This is indicated in step 1 of Table 3.

TABLE 3 Summarization Algorithm of Open-type Questions 1. Employ the clustering algorithm on answers 2. Extract the noun phrases in every cluster by using a shallow parser. 3. For each cluster and each label (or noun phrase), calculate the score by using the Relevance Scoring Function: ${\sum\limits_{w}\;{{p\left( w \middle| \theta \right)}{{PMI}\left( {w,\left. l \middle| C \right.} \right)}}} - {D\left( \theta \middle| C \right)}$ Where θ is the cluster, w is the word, l is the label or noun phrase, C is the background context which is composed of 5,000 questions in the same category, p(·) is conditional probability, PMI(·) pointwise mutual information, and D(·) is KL-divergence 4. Extract the key answer which contains the noun phrase that has the highest score in each cluster 5. Rank these key answers by the cluster size and present the results.

Next, in order to understand the main idea of each cluster, salient noun phrases can be extracted using any of a wide variety of shallow parsing components. This is indicated in step 2 of Table 3. Each label (or noun phrase) in each cluster can then have a relevance score calculated for it, such as by using a known first-order relevance scoring function also sometimes referred to as automatic labeling of multi-nomial topic models. This is set out in step 3 in Table 3. The relevance function can be used to extract a key answer that contains the noun phrase that has the highest score (calculated in step 3) in each cluster, and the key answers can also be ranked, within each cluster, based on the relevance score. This is indicated by step 4 in Table 3.

Finally, the summarized answer can be presented to the user, by extracting the most important answer in each cluster, and sorting those most important answers according to the cluster size that spawned the answers. This is indicated by step 5 in Table 3.

Table 4 illustrates an example of summarization of results of an open-type question. The question asked methods of changing a first style of operating system to a second style. There are many kinds of software that have this functionality. The best answer to the question only listed a single choice—The ACMEDOCK product while other answers suggested ABC and DEF and GHI products. The summarized answer contains a variety of software that have this functionality and a web page link that lists all kinds of software for turning the first style of operating system into the second style.

TABLE 4 Summarization Result of Open-Type questions Question (http://answers.alpha.com/question/?qid=1005120801427) What is the best way to make 123 look like Mac osX? Best Answer Chosen I found the best way to do this is to use BetaBlinds. A program that, if you use the total AMCEDock, package will allow you to add the ObjectBar in addition to changed the toolbars to be OPSX stylized. If you want added functionality you can download programs off the internet that will mimic the Expose feature which will show you a tiled set of all open windows. Programs that will do this include: ABC, DEF, and GHI Auto-summarized Answer An additional option is JKL - a “Shell Replacement” for ABC that has a variety of themes you can install. Undoubtedly there are various OPSX themes available for JKL. I have included a source to a max OPSX theme for JKL at customize.org. DOE is a transformation pack and the most comprehensive in terms of converting an XP system's look to that of an OPSX system, google it up and you should find it, v3 seems to be in development and should be out soon. Beta http://www.ACMEdock.com/products/Beta . . . Manually-summarized Answer There are multiple ways of solution. One is to use Beta. The package will allow you to add the ObjectBar for changing to the OPSX theme. You can also make added functionality of Expose feature by downloading the programs like ABC, DEF and GHI. The download URL of it is http://www.ACMEdock.com/products/beta/. Another option is to use DOE which is a transformation pack. The third Option is the JKL, it is a “Shell Replacement” for windows that has a variety of OPSX tehmes you can install. The download url is http://JKL.net and I have included a source of OPSX theme for JKL at http://www.customize.org/details/33409. To have a complete study of the methods of converting 123 style to OPSX style, you may also refer to http://features.JKEL.com/entry/8828351836181248/ for detail information.

Table 5 shows a question-focused answer summarization technique for opinion type questions. Opinion type questions can be divided into two subcategories. One is sentiment-oriented questions that ask the sentiment about something. For instance, “What do you think of . . . ” is a sentiment type question. The other type of opinion question is a list-oriented question that requests a list of answers and looks to determine which item is most popular. In sentiment-oriented questions, the askers likely care about how many people support or oppose something. Table 5 shows steps for summarizing answers to sentiment-type questions. Table 6 shows steps for summarizing list-type questions. Tables 7 and 8 show summaries generated for sentiment-type questions and list-type questions, respectively. Of course, the methods in Tables 5 and 6 and the summaries in Tables 7 and 8 are exemplary only.

TABLE 5 Summarization Algorithm of Sentiment Questions For each answer 1. If an answer begins with negative cue phrase (e.g. “No, it isn't” etc.), it is annotated as Against. If a response begins with positive cue phrase (e.g. “Yes, it is” etc.), it is annotated as Support. 2. For a clause, if number of positive sentiment word is larger than negative sentiment word, the sentiment of the clause is Positive. Otherwise, the sentiment of the clause is Negative. 3. If there are negative indicators such as “don't/never/ . . . ” in front of the clause, the sentiment should be reversed. 4. If number of negative clauses is larger than number of positive clauses, the sentiment of the answer is Negative. Otherwise, the sentiment of the answer is Positive. 5. Denote the sentiment value of question as s(q), the sentiment value of an answer as s(a), and then the final sentiment of the answer is logical AND of s(q) and s(a)

TABLE 6 Summarization Algorithm of List Questions 1. Segment the answers into sentences 2. Cluster sentences 3. For each cluster, Choose the key sentence based on mutual information between itself and other sentences within the cluster 4. Rank the key sentences by the cluster size

TABLE 7 Summary of Sentiment-Opinion Question Question (http://answers.Alpha.com/question/?qid=1006050125145) I am 16 and i stil talk to my erm..teddy bear . . . am i wierd??? Best Answer Chosen not at all i'm 14 and i too do that Auto-summarized Answer A: No, you're not weird . . . you're Pratheek! :D (Against) A: no, i like to hold on to my old memories too. i do it sometimes too. (Against) A: It's might be a little uncommon for a 16 year old to talk to a teddy bear but there would be a serious problem if you told me that your teddy bear answered back as you talked to him!!:) (Support) A: You still has a teddy bear, wow that really uncommon for a boy your age to have a teddy bear. But I GUESS IT GOOD because you talk to it. (Support) A: Yes. (Support) A: i talk to a seed im growing .. its not weird . . . :) (Support) A: It will get weird when he starts to answer back! (Against) A: not really. it depends how you talk i mean not if you talk to it like its a little kid like my brother does. (Against) A: I slept with my teddy bear until I graduated. Can't say that I ever had a conversation with him, but if I had I'm sure he would've been a very good listener. (Support) A: You may pass GO . . . (Neutral) A: not at all i'm 14 and i too do that (Aganist) Overall Attitude: Support 5 Votes Neutral 1 Votes Against 5 Votes Manually-summarized Answer against (vote 5) reasons: i like to hold on to my old memories too. (vote 1) I slept with my teddy bear until I graduated. (vote 1) i'm 14 and i too do that (vote 1) support (vote 4) neuter (vote 2)

TABLE 8 Summary of List-Opinion Question Question (http://answers.ABC.com/question/?qid=20060718083151AACYQJn) What is the best sci-fi movie u ever saw? Best Answer Chosen Independance Day Auto-summarized Answer DEF Movie (5) GHI Movie (3) fi movie has to be JKL Movie (2) But the best “B” sci (2) I liked MNO Movie it didn't scare me and I though they did a great job receating Egypt (3) PQR Movie (3) Manually-summarized Answer DEF Movie (vote 6); GHI Movie (vote 3); JKL Movie (vote 2); MNO Movie (vote 2); PQR Movie (vote 2); STU Movie (vote 2); VWX Movie v.s XYZ Movie (vote 1); ABC123 Movie (vote 1); DEF456 Movie(vote 1); GHI789 Movie(vote 1); JKL123 Movie (vote 1); MNO456 Movie (vote 1); PQR789 Movie (vote 1); STU123 Movie (vote 1); VWX456 Movie (vote 1)

As shown in Table 5, Answers are summarized by first determining whether they begin with a negative queue phrase or a positive queue phrase. This can be done simply by using an opinion-word dictionary that is a dictionary of opinion words, and a queue phrase list that lists queue phrases into positive and negative queue phrases. If the answer beings with a negative queue phrase, it is annotated as being “against” what is proposed, and if it begins with a positive queue phrase it is annotated as “supporting” what is proposed.

If the answer cannot be annotated in step 1, then, for a clause, answer summarizer 106 determines whether a number of positive sentiment words in the clause is larger than the number negative sentiment words. If so, the clause is annotated “positive” and if not, it is annotated “negative”.

In step 3, if there are negative indicators such as “don't”, “never”, etc. in front of the clause, then the sentiment is reversed.

In step 4, if the number of negative clauses is larger than the number of positive clauses in the answer, then the sentiment of the answer is “negative”. Otherwise, it is “positive”.

Finally, in step 5 of Table 5, the sentiment value of the question (is denoted as S(Q)), and the sentiment value of the answer is denoted as (S(A)). The final sentiment of the answer is the logical Boolean AND of S(Q) and S(A).

In Table 6, the steps for summarizing answers to list questions include first segmenting the answers into sentences and clustering the sentences using the clustering technique set out above in Table 3. This is indicated by steps 1 and 2 in Table 6.

Next, for each cluster, a key sentence based on mutual information between itself and other sentences within the cluster is chosen. The key sentences are then ranked by cluster size that spawned the key sentences. This is indicated by steps 3 and 4 in Table 6.

For the list-oriented questions, it can be seen that a simple counting algorithm tallies different answers of questions, together with their supporting votes, to obtain the answer summaries.

It can be seen that Table 7 presents the summarization result of a sentiment-oriented question which asks whether it is strange for a 16 year old child to talk to a teddy bear. The best answer is a negative response. However, if all answers are considered, it can be seen that half of the answers agree, but another half of them disagree. This tally is thus presented in the answer summarization.

Table 8 shows the summarization result of a list-oriented question which asks for users to identify the best science fiction movie they have ever seen. The best answer just gives a single choice, while the summarized answer gives a list of best science fiction movies with the number of supporting votes.

FIG. 3 is a block diagram illustrating a runtime question answering system 350 which can be used to present answers, to previously asked questions, to a user who submits a current question. FIG. 4 is a flow diagram illustrating one embodiment of the overall operation of the system shown in FIG. 3.

Question search component 354 first receives an input question 352 from a user. This is indicated by block 380 in FIG. 4. Component 354 then identifies, within data store 110 (that contains questions with summarized answers) whether any questions in data store 110 are similar to question 352. This is indicated by block 382 in FIG. 4. In doing so, component 354 can use any of a wide variety of different techniques that are used to identify similarity between two textual inputs. For instance, component 354 can identify the question type and keywords in the question and compare that with the question type and keywords (content) in data store 110. Of course, other more sophisticated natural language processing techniques can be used as well. For instance, component 354 can convert the input question 352 into a syntax parse tree or logical form (content), and that can be compared to syntax parse trees or logical forms generated for the questions in data store 110. A wide variety of other processing techniques can be used as well, in order to identify questions in data store 110 that are similar to input question 352.

In any case, once the similar questions are identified in data store 110, the answers to those questions are retrieved from data store 110. This is indicated by block 384 in FIG. 4. Component 354 then outputs the question and answer summaries 356, for review by the user. This is indicated by block 386 in FIG. 4. Of course, component 354 can also allow the user to interact with the summarized answers, such as to identify a best answer among them, or allow the community to define the best answer among them, etc.

It should also be noted that question search component 354 can be any type of information search component, such as an information retrieval system. In that type of system, data store 110 may be a valuable repository of knowledge that can be searched in response to an information retrieval query. Therefore, system 354 is not limited to a community question answering system, but can be any other type of information seeking system as well, that accesses the data stored in store 110 based on community based question answering services.

With reference to FIG. 5, an exemplary system for implementing some embodiments includes a general-purpose computing device in the form of a computer 510. Components of computer 510 may include, but are not limited to, a processing unit 520, a system memory 530, and a system bus 521 that couples various system components including the system memory to the processing unit 520. The system bus 521 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus.

Computer 510 typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer 510 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer 510. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer readable media.

The system memory 530 includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM) 531 and random access memory (RAM) 532. A basic input/output system 533 (BIOS), containing the basic routines that help to transfer information between elements within computer 510, such as during start-up, is typically stored in ROM 531. RAM 532 typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit 520. By way of example, and not limitation, FIG. 5 illustrates operating system 534, application programs 535, other program modules 536, and program data 537. The systems discussed above in FIGS. 1-4 can be stored in other program modules 536 or elsewhere, including being stored remotely.

The computer 510 may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only, FIG. 5 illustrates a hard disk drive 541 that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive 551 that reads from or writes to a removable, nonvolatile magnetic disk 552, and an optical disk drive 555 that reads from or writes to a removable, nonvolatile optical disk 556 such as a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive 541 is typically connected to the system bus 521 through a non-removable memory interface such as interface 540, and magnetic disk drive 551 and optical disk drive 555 are typically connected to the system bus 521 by a removable memory interface, such as interface 550.

The drives and their associated computer storage media discussed above and illustrated in FIG. 5, provide storage of computer readable instructions, data structures, program modules and other data for the computer 510. In FIG. 5, for example, hard disk drive 541 is illustrated as storing operating system 544, application programs 545, other program modules 546, and program data 547. Note that these components can either be the same as or different from operating system 534, application programs 535, other program modules 536, and program data 537. Operating system 544, application programs 545, other program modules 546, and program data 547 are given different numbers here to illustrate that, at a minimum, they are different copies.

FIG. 5 shows the clustering system in other program modules 546. It should be noted, however, that it can reside elsewhere, including on a remote computer, or at other places.

A user may enter commands and information into the computer 510 through input devices such as a keyboard 562, a microphone 563, and a pointing device 561, such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 520 through a user input interface 560 that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). A monitor 591 or other type of display device is also connected to the system bus 521 via an interface, such as a video interface 590. In addition to the monitor, computers may also include other peripheral output devices such as speakers 597 and printer 596, which may be connected through an output peripheral interface 595.

The computer 510 is operated in a networked environment using logical connections to one or more remote computers, such as a remote computer 580. The remote computer 580 may be a personal computer, a hand-held device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer 510. The logical connections depicted in FIG. 5 include a local area network (LAN) 571 and a wide area network (WAN) 573, but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet.

When used in a LAN networking environment, the computer 510 is connected to the LAN 571 through a network interface or adapter 570. When used in a WAN networking environment, the computer 510 typically includes a modem 572 or other means for establishing communications over the WAN 573, such as the Internet. The modem 572, which may be internal or external, may be connected to the system bus 521 via the user input interface 560, or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer 510, or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation, FIG. 5 illustrates remote application programs 585 as residing on remote computer 580. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. 

1. A system for summarizing answers to previously asked questions, the system comprising: a question type identifier component accessing a question data store of previously asked questions along with answers submitted to the previously asked questions and selecting a question for answer summarization and identifying a question type of the selected question; an answer summarization component generating answer summaries that summarize the answers to the selected question, the answer summaries being in a format that is determined by the question type of the selected question; a summary data store, wherein the answer summarization component stores the selected question and the answer summaries generated for the selected question in the summary data store for subsequent retrieval by comparing a question type and subject matter content of an input question to the identified question type of the selected question and subject matter content of the selected question; and a computer processor, being a functional part of the system, activated by the question type identifier component and the answer summarization component to facilitate identifying a question type and generating answer summaries.
 2. The system of claim 1 wherein the answer summarization component selects one of a plurality of different question summarization algorithms to generate the answer summaries for the answers to the selected question based on the question type such that answers that are in a format inconsistent with the question type are revised to be in the format that is determined by the question type.
 3. The system of claim 1 wherein there are a plurality of answers to the selected question in the question data store.
 4. The system of claim 3 wherein the plurality of answers to the selected question include a subset of answers identified by at least an asker of the selected question as best answers to the selected question, and wherein the answer summarization component identifies the best answers in the answer summaries.
 5. The system of claim 4 wherein the answer summarization component generates the answer summaries from the plurality of answers, including the subset of best answers to the selected question.
 6. The system of claim 4 wherein the question data store comprises questions and answers submitted in a community based question answer system wherein the best answers are identified by at least one user in a community of users and an asker of the selected question.
 7. The system of claim 1 and further comprising: a question search component receiving the input question and identifying one or more similar questions in the summary data store and outputting, in response to the input question, the answer summaries for the one or more similar questions.
 8. The system of claim 7 wherein the question search component is configured to identify the one or more similar questions as having a similar question type to a question type of the input question and having similar content to content of the input question.
 9. The system of claim 1 wherein the question type identifier is configured to identify question type by mapping the selected question to a question type taxonomy dividing the previously asked questions into different types.
 10. A computer implemented method of answering an input question using a computer with a processor, comprising: accessing, with the processor, a plurality of previously asked questions along with answers previously submitted for the previously asked question; selecting one of the previously asked questions; identifying, with the processor, a question type of the selected question, the question type being one of a plurality of different question types, the plurality of different question types being independent of a subject matter content requested by the selected question; selecting, with the processor, an answer summarization component to generate answer summaries for the answers to the selected question based on the question type identified, such that if an answer for which a summary is to be generated is in a format inconsistent with the question type identified, the answer summary for the answer is generated in a format consistent with the question type identified, and storing the answer summaries; receiving the input question and determining a question type and a subject matter content of the input question; comparing, with the processor, the input question to previously asked questions for which answer summaries have been generated; and outputting, with the processor, in response to the input question, the answer summaries generated from answers to previously asked questions that are similar to the input question in both question type and subject matter content.
 11. The method of claim 10 wherein selecting an answer summarization component comprises: selecting one of a plurality of different answer summarization components, each answer summarization components generating answer summaries for a given answer in a different format, regardless of a format of the given answer.
 12. The method of claim 10 wherein the answers to the selected question have a subset of the answers identified by at least an asker of the selected question as best answers and wherein the answer summaries identify the best answers.
 13. The method of claim 10 wherein identifying a question type comprises: applying an automatic question type identifier algorithm to the selected question to identify the question type.
 14. A question answering system, comprising: an answer summarization system generating answer summaries for answers to a plurality of stored questions, the answer summaries having a format that is determined based on a type of the question that elicited the answer; a question search component receiving an input question, identifying a type of the input question, identifying similar stored questions based on the type of the input question, based on types of the stored questions and based on content of the input question and the stored questions, and the question search component further to outputting the answer summaries generated for the similar stored questions, in response to the input question; and a computer processor being a functional component of the question answering system and activated by the answer summarization system and the question search component to facilitate generating answer summaries, identifying a question type and identifying similar stored questions.
 15. The question answering system of claim 14 wherein the answer summarization system comprises: a question type identifier component receiving a user change to a user interface, the user change identifying a question type of the selected question.
 16. The question answering system of claim 15 wherein the answer summarization system comprises: an answer summarization component generating the answer summaries in the format determined by the question type.
 17. The question answering system of claim 16 wherein the answer summarization component comprises: a plurality of different answer summarization modules, each generating the answer summaries in a different format, one answer summarization module corresponding to each different question type. 